In Silico ADME, Bioactivity, Toxicity Predictions and Molecular Docking Studies Of A Few Antidiabetics Drugs

Abstract

Computer-aided drug design (CADD) methodologies have become essential in modern drug discovery, offering significant cost and time efficiency. By analyzing the physicochemical properties, bioactivity, and toxicity profiles of existing drugs, researchers can identify key pharmacophoric features crucial for designing novel compounds targeting specific biological pathways. In this study, 27 FDA-approved antidiabetic drugs (since 1957) were selected for computational analysis. Their physicochemical properties, bioactivity scores, and toxicological profiles were predicted using various tools, including Molinspiration, SwissADME, Osiris Properties Explorer, and pkCSM. Additionally, molecular docking studies were performed using CB-Dock against target proteins with PDB IDs: 4YVV, 5G5J, 5Y2T, 7Y1J, 6B1E, 2PDK, 5NN6, 7Y0B, and 3UA1. Among the selected compounds, 25 adhered to Lipinski’s Rule of Five, suggesting good oral bioavailability. However, certain compounds exhibited toxicity risks, such as mutagenicity and tumorigenicity, as predicted by the Osiris Properties Explorer. Molecular docking facilitated the investigation of drug-target interactions, providing insights into binding mechanisms. These findings contribute to the rational design of novel antidiabetic drugs with improved efficacy and safety.

Keywords

Introduction

       
           
       
Figure 1. Stages Of Traditional Drug Discovery and Computer-Aided Drug Design Tools

Drug failures often stem from poor efficacy, side effects, unfavourable pharmacokinetics, or commercial limitations.³ The increasing use of in-silico chemistry and molecular modeling has revolutionized drug design. Drug-likeness, a key concept in CADD, depends on molecular properties such as hydrophobicity, electronic distribution, hydrogen bonding, molecular size, and flexibility. Lipinski’s Rule of Five (RO5) provides guidelines for assessing a molecule’s pharmacokinetic properties, such as absorption, distribution, metabolism, and excretion (ADME). ^4,5 These properties can be predicted before synthesis, optimizing drug design efficiency. Molecular docking plays a crucial role in predicting drug-target interactions by simulating ligand binding orientations. ⁶ Typically, the receptor remains rigid while ligand conformations adjust for optimal binding. Overall, computational tools have transformed drug discovery by enhancing efficiency, reducing costs, and enabling researchers to address complex challenges that traditional methods alone cannot efficiently resolve. Antidiabetic drugs effectively lower blood sugar levels, preventing complications like retinopathy, neuropathy, and nephropathy while alleviating symptoms such as excessive thirst, urination, and ketoacidosis. Diabetes is a carbohydrate metabolism disorder caused by insulin deficiency or resistance, disrupting blood glucose regulation. Normal fasting blood glucose levels range between 70–100 mg/dL. Type 1 diabetes results from insulin deficiency and is treated with subcutaneous insulin injections. Type 2 diabetes, the most common form, arises from insulin resistance. Treatments aim to (1) stimulate insulin secretion, (2) enhance insulin sensitivity, (3) reduce glucose absorption, and (4) promote glucose excretion through urine. ^7,8 Numerous antidiabetic drugs on the market target different receptors. While many small molecules with antidiabetic potential have been identified and are undergoing clinical trials, their use is often limited by side effects. This highlights the need for new, potent antidiabetic agents with improved pharmacological profiles.^9-12 Prompted by above findings, a few US FDA approved antidiabetic drugs were selected and their in silico properties were determined by various computational tools to predict physicochemical properties, bioactivity, in silico binding affinity and toxicity profiles in the present investigation.

Table 1. Details of selected compounds

1.Tolbuatmide	2. Glibenclamide	3. Glipizide
4. Gliclazide	5. Glimepiride	6. Chlorpropamide
7. Tolazamide	8. Acetohexamide	9. Metformin
10. Rosiglitazone	11. Pioglitazone	12. lobeglitazone
13. Repaglinide	14. Nateglinide	15. Sitagliptin
16. Saxagliptin	17. Teneligliptin	18. Alogliptin
19. Linagliptin	20. Sorbinil	21. Acarbose
22. Miglitol	23. Dapagliflozin	24. Canagliflozin
25. Empagliflozin	26. Ertugliflozin	27. Bromocriptine

Table 3. Physicochemical properties of selected antidiabetic drugs (1-27)

Table 4. Bioactivity, bioavailability and synthetic accessibility data predicted for selected antidiabetic drugs (1-27)

Table 5. Toxicity data predictions using Osiris property explorer and pk CSM tools

Table 6. Binding energies of selected drugs using CB Dock

IV. CONCLUSION

The computational analysis of 27 FDA-approved antidiabetic drugs (1-27) provided valuable insights into their physicochemical properties, bioactivity, toxicity, and molecular docking interactions. Twenty-five compounds adhered to Lipinski’s Rule of Five, suggesting good oral bioavailability, while Acarbose and Bromocriptine exhibited poor absorption due to multiple violations. Drug-likeness scores were highest for Teneligliptin, Miglitol, Sorbinil, and Rosiglitazone. Bromocriptine showed moderate GPCR ligand activity, and Saxagliptin exhibited the highest protease inhibition. Toxicity predictions revealed mild to moderate risks, with some drugs showing hepatotoxicity and reproductive effects. Molecular docking studies indicated strong binding interactions for Glimepiride with 4YVV (-11.9 kcal/mol) and Thiazolidinediones with 5Y2T. These findings highlight the potential of computational tools in drug discovery, aiding in the identification of safer and more effective antidiabetic agents.

ACKNOWLEDGEMENTS

The authors are grateful to the Principal, Gokaraju Rangaraju College of Pharmacy and the Gokaraju Rangaraju Educational Society for providing necessary facilities.

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